Drone Delivery Routing with Stochastic Urban Wind
Drone Delivery Routing with Stochastic Urban Wind
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DOI：
10.1109/itsc57777.2023.10422533
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发表时间：
2023-09
期刊：
影响因子：
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通讯作者：
Minghao Chen;Andrew W. Smyth;M. Giometto;Max Z. Li
中科院分区：
文献类型：
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作者：
Minghao Chen;Andrew W. Smyth;M. Giometto;Max Z. Li
由于低空风场在空间和时间上的可变性，城市地区对无人机交付提出了独特的挑战。在本文中，我们提出了一种新的方法来应对这些挑战，并确保在多风的城市环境中安全可靠的无人机交付路线。我们的方法结合了随机和空间异质性的城市风场，使用基于随机规划和概率建模技术。我们开发了随机混合整数线性规划（SMILP），将通过大涡模拟（LES）生成的真实风场景与随机无人机路由问题（SDRP）相结合。我们采用模拟退火（SA）算法，有效地探索解决方案的空间，并在短时间内处理数百个客户的请求。我们比较SA的实施与SMILP实施的小情况下的性能，并显示其优越性的解决方案的质量。通过对大规模实例的仿真和性能评估，我们证明了我们的方法在不同的风力条件和客户数量下的有效性。我们还进行了敏感性分析，以调查操作高度的影响（即，风场的高度）对我们方法的性能的影响。我们的研究结果为在多风的城市环境中优化无人机路由决策提供了有价值的见解。
Urban areas present unique challenges for drone-based deliveries due to the spatially and temporally variable nature of the wind field at low altitudes. In this paper, we propose a novel approach to address these challenges and ensure safe and reliable drone delivery routings in windy urban environments. Our approach incorporates the stochastic and spatially heterogeneous urban wind field using scenario-based stochastic programming and probabilistic modeling techniques. We develop stochastic mixed integer linear programs (SMILPs) that integrate realistic wind scenarios generated via large-eddy simulations (LESs) with the stochastic drone routing problem (SDRP). We employ the simulated annealing (SA) algorithm, which effectively explores the solution space and handles hundreds of customers' requests in a short time. We compare the performance of SA implementation with SMILPs implementation on small cases and show its superiority in terms of solution quality. Through simulations and performance evaluations on large-scale instances, we demonstrate the effectiveness of our approach under different wind conditions and number of customers. We also conduct a sensitivity analysis to investigate the influence of operational altitude (i.e., the height of the wind field) on the performance of our method. Our results provide valuable insights for optimizing drone routing decisions in windy urban environments.